@Article{CiCP-31-3,
author = {Yunwen, Yin and Yin, Weishi and Pinchao, Meng and Liu, Hongyu},
title = {On a Hybrid Approach for Recovering Multiple Obstacles},
journal = {Communications in Computational Physics},
year = {2022},
volume = {31},
number = {3},
pages = {869--892},
abstract = {<p style="text-align: justify;">In this paper, a hybrid approach which combines linear sampling method
and the Bayesian method is proposed to simultaneously reconstruct multiple obstacles. The number of obstacles and the approximate geometric information are first
qualitatively obtained by the linear sampling method. Based on the reconstructions of
the linear sampling method, the Bayesian method is employed to obtain more refined
details of the obstacles. The well-posedness of the posterior distribution is proved by
using the Hellinger metric. The Markov Chain Monte Carlo algorithm is proposed to
explore the posterior density with the initial guesses provided by the linear sampling
method. Numerical experiments are provided to testify the effectiveness and efficiency
of the proposed method.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2021-0124},
url = {https://global-sci.com/article/79530/on-a-hybrid-approach-for-recovering-multiple-obstacles}
}